Dissertation
A “meta” analysis: the proper usage of computational biology and bioinformatics in infectious disease research
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2021
DOI: 10.17077/etd.005941
Abstract
The thesis which this abstract precedes is not the typical sort. As a thesis written in the fulfillment of a PhD in microbiology, the primary focus of the work was to examine host-pathogen interactions and the transmission of infectious diseases. However, with a growing interest in computational techniques, the focus was expanded to include a critical examination of the analyses and interpretations of our work as well as an examination of the integration of computational techniques and traditional “wet-bench” experiments. Thus, the nature of this thesis is quite “meta” to say the least. Our organisms of interest—the model organisms for which we could critically evaluate the proper usage of computational biology and bioinformatics in infectious disease research—were Leishmania spp. parasites and SARS-CoV-2.Leishmania spp. parasites cause a spectrum of diseases which includes the potentially fatal visceral leishmaniasis. The manifestation of the diseases is dependent upon the infecting parasite species, and this visceral form is often caused by Leishmania infantum or L. donovani. These parasites are transmitted between hosts by female, Phlebotomine sand flies. Because we previously observed that disruption of the sand fly midgut microbiota using antibiotics reduced parasite growth and development, we sought to determine whether specific factors in the sand fly midgut microbiota might influence the growth of L. infantum parasites. Using 16S rDNA sequencing, the microbiomes of antibiotic-treated sand flies did not have significantly altered alpha or beta diversity. However, the relative frequency of several bacterial taxa was changed in the midguts of these flies. Reflecting upon this project, several factors were considered. The sample size and the use controls exemplified the importance of experimental planning prior to project start. And, thoughtfully considering the computational analysis, in this instance rarefaction, became a clear and necessary step which should be included in all project planning.
We additionally sought to evaluate disease severity and begin to define the liver microenvironment in L. infantum-infected mice fed one of three high lipid diets. Previous studies had indicated that high lipid intake reduced parasite burden and disease severity during several parasitic infections, including L. donovani infection. But alterations to the liver microenvironment due to dietary lipids had not been fully characterized in the context of these infections. In our experiments, we observed significantly altered parasite kinetics in the livers of mice fed high lipid diets. Microarrays performed on liver tissues revealed significantly increased expression of transcripts belonging to immune-related pathways in infected mice fed a diet high in fat and cholesterol. We adamantly acknowledge that this study was “hypothesis-finding” rather than “hypothesis-testing”, as with other large data studies. While mechanistic conclusions cannot be drawn in full from this study, the results serve to form new hypotheses to be tested.
Late in 2019, a pneumonia-like disease, soon to be called COVID-19, was reported. The novel virus, a coronavirus later named SARS-CoV-2, quickly traversed the globe, and the WHO declared a pandemic. Heterogeneous policies to mitigate SARS-CoV-2 transmission existed across the United States in the spring of 2020. One such statewide policy mandated citizens stay in their place of residence. Thus, we hypothesized that the presence of these particular statewide orders impacted the transmission of SARS-CoV-2 in the Midwest region where these orders were most heterogeneous and the population metrics vary greatly. We found that the risk of COVID-19 was lower in Midwestern states with statewide orders to “stay at home” compared to Midwestern states without these orders. Additionally, the states with statewide orders to stay at home had less COVID-19 incident cases per population density than states without. The analyses included in this project emphasized the importance of project planning to address hypotheses effectively and emphasized the implications of avoiding over complicated data analyses.
Details
- Title: Subtitle
- A “meta” analysis: the proper usage of computational biology and bioinformatics in infectious disease research
- Creators
- Ellen Taylor Kiser
- Contributors
- Mary Wilson (Advisor)Noah Butler (Committee Member)Lilliana Radoshevich (Committee Member)Dominique Limoli (Committee Member)Christine Petersen (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Microbiology
- Date degree season
- Summer 2021
- DOI
- 10.17077/etd.005941
- Publisher
- University of Iowa
- Number of pages
- xviii, 171 pages
- Copyright
- Copyright 2021 Ellen Taylor Kiser
- Language
- English
- Description illustrations
- color illustrations
- Description bibliographic
- Includes bibliographical references (pages 136-171).
- Public Abstract (ETD)
While this thesis is written in fulfillment of a PhD in microbiology, the details should be useful to scientists across a broad range of disciplines…well, at least I hope they are. My colleagues and I initially sought to study a disease called leishmaniasis which impacts people across the globe and coincides with areas of poverty. We hoped to understand aspects of transmission and ways diet impacts severity of the disease. And because some of the work for this thesis took place during the COVID-19 pandemic, we decided to study how government policies impacted transmission of the virus that causes COVID-19. During and after the computer-based portion of these studies, we considered how the experiments could have been improved. We also evaluated the conclusions we formed from the experiments…quite “meta” if you ask me.
Due to advancing technology, the study of microbes—and many other fields of biology—are changing quickly. Integrating these technological advances and new forms of analyses with traditional ways that we study microbes has allowed for better comprehension of the diseases the microbes cause. But the new computer-based analyses can be difficult to perform and the resulting information difficult to understand. So, to any scientists reading this thesis or even just this abstract, here is the take-home message:
Meticulously plan experiments AND the analysis of those experiments. Carefully interpret the experiments as well as critically evaluate the conclusions so they can be usefully applied to life outside the laboratory.
- Academic Unit
- Microbiology and Immunology
- Record Identifier
- 9984124268102771
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